Authors:K. Gayathri, K. ShailendhraPages: 3 - 28Abstract: To understand the role of medically significant hemodynamic wall parameters in the pathogenesis of vascular diseases, pulsatile blood flow in large human arteries of systemic, pulmonary and coronary circulation is investigated by mathematical modelling. To be medically realistic, the pressure gradient waveforms reported in the cardiology literature for the arteries considered are digitised and developed in Fourier series (McDonald's model). Three objectives of the article are to (i) compare qualitatively and quantitatively the pulsatile blood flow between the parallel plate and circular geometry, (ii) compare the hemodynamic wall parameters in the three major circulations mentioned above to gain new medical or physiological insights and (iii) understand if slip at the wall has significant influence on the hemodynamic wall parameters. Our model is reliable since the results obtained here through exact solutions are in great agreement with those reported in the medical literature. New insights gained from our study, documented here for the first time in the hemodynamic literature, are as follows: parallel plate geometry approximation is not reliable quantitatively; larger the radius (Womersley number), larger is the value of relative residence time and hence, higher the probability for vascular diseases; none of the commonly employed interface conditions are suitable for the hemodynamic studies. Comparing our results with earlier studies, we recommend that future research should focus on developing an interface condition exclusively for haemodynamics. We support the recent understanding that low wall shear stress and high oscillatory shear index need not co-locate. We have rendered new physiological insight for this result.Keywords: Haemodynamics; oscillatory shear index; relative residence time; saffman slip condition; wall shear stressCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 3 - 28PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089183Issue No:Vol. 14, No. 1/2 (2018)

Authors:R.S. Jeena, Sukesh KumarPages: 29 - 48Abstract: The innovative developments in the field of machine intelligence have paved way to the growth of tools for assisting physicians in disease diagnosis. Early diagnosis and prognosis of stroke are crucial for timely prevention and cure. This research work focuses on the design of a stroke prediction system by investigating the various physiological parameters that are used as risk factors. Features extracted from various risk parameters carry vital information for the prediction of stroke. Classification algorithm that has been used with the number of attributes for prediction are support vector machines (SVM) and artificial neural network. Data collected from international stroke trial database was successfully trained and tested using both classifiers. The predictive models discussed here are based on different supervised machine learning techniques as well as on different input features and data samples. SVM gave an accuracy of 91% while neural network outperforms SVM by providing an accuracy of 98.1%.Keywords: artificial neural network; stroke; support vector machineCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 29 - 48PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089192Issue No:Vol. 14, No. 1/2 (2018)

Authors:Santosh Kumar, G. SahooPages: 49 - 69Abstract: In today's present scenario, heart disease has greater impact on our lives and identified fatal due to its high mortality rate. The diagnosis of heart disease is more challenging due to its vulnerability. Gone to limitation of previous work of literature survey, enhanced decision tree algorithm is introduced and applied on University of California, Irvine data sets. In order to predict heart disease, enhanced decision tree algorithm generates the decision rules which are later optimised by genetic algorithm. By then we examine the methods and operators of the algorithm. Finally our proposed algorithm is compared with decision tree (C4.5) and support vector machine algorithm, the proposed algorithm shows high accuracy, and its simplicity makes ideal for pattern recognition applications.Keywords: C4.5; cardiovascular disease; CVD; GA; genetic algorithm; heart disease; SVMCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 49 - 69PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089199Issue No:Vol. 14, No. 1/2 (2018)

Authors:K. Gayathri, K. ShailendhraPages: 70 - 89Abstract: An attempt is made to investigate whether the static magnetic field (SMF) employed in magnetic resonance imaging (MRI) have any adverse effect on the hemodynamic wall parameters in large arteries or not. With the intention of addressing the controversy in the safety issues during MRI exposure, haemodynamics and pathology of large arteries, such as brachial, femoral and pulmonary artery, are compared by varying the intensity of SMF from high to ultrahigh. To be more medically accurate physiological pressure gradient waveforms taken from cardiology literature were digitised and adequate number of harmonics were extracted in order to represent them as Fourier series. All the medically relevant parameters related to endothelial functioning are significantly affected during the time of exposure to ultrahigh intensity SMF, irrespective of the fact whether the artery is closer or away from the heart. In such fields, the fluctuation of Wall Shear Stress (WSS) vector in pulmonary artery is too severe as inferred from oscillatory shear index (OSI) values. The common hypothesis that low WSS and high OSI colocate is not acceptable both in the absence and the presence of magnetic field. It is also inferred that relative residence time can be considered as a single robust metric to predict the pathogenesis of vascular diseases when OSI is moderate. It is felt that more research is necessary, especially to clarify many existing contradictory results in this regard. The controversial reports in the literature of SMF motivated us to mathematically investigate the possible adverse effects of ultrahigh SMFs on pulsatile blood flow in large human arteries and find the maximum intensity of SMF up to which the blood flow and other medically relevant parameters are not significantly affected.Keywords: Haemodynamics; magnetic resonance imaging; oscillatory shear index; relative residence time; ultrahigh intensity magnetic field; wall shear stressCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 70 - 89PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089227Issue No:Vol. 14, No. 1/2 (2018)

Authors:Cifha Crecil Dias, Surekha Kamath, Sudha VidyasagarPages: 90 - 103Abstract: In the current industrialized era, diabetes mellitus is spread worldwide, a metabolic disease termed as diabetes. In this condition, human body is not able to maintain an acceptable range 70-180 mg/dl of blood glucose and could lead to prolonged sickness. Diabetes at least doubles a person's risk of death. As the years progress by 2035 the expected death shall rise to 592 million. Several researches especially in the regulation of blood glucose were carried out and still remained an open challenge. With the increase in the modern technology and treatment towards the diabetes mellitus, there are several open opportunities to develop different methods of treatment. The main emphasize is to develop an efficient controller to the device which mimics the human pancreas. The elimination of the risk of occurrence of hypoglycaemia is the main concern. Though many Artificial Pancreas systems are available, they are still subjected to many limitations. The challenges in development of Artificial Pancreas are choosing an appropriate mathematical model while developing an efficient control algorithm. Various mathematical models and methodological approach are reviewed and elaborated in this paper.Keywords: artificial pancreas; Bergman model; Cobelie model; control strategy; diabetes mellitus; Hovorka model; hypoglycaemia; insulin pump; mathematical plant models; Sorenson modelCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 90 - 103PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089229Issue No:Vol. 14, No. 1/2 (2018)

Authors:Dayakshini Sathish, Surekha Kamath, Keerthana Prasad, Rajagopal KadavigerePages: 104 - 118Abstract: Breast cancer is the leading cancer in women worldwide. Early detection can reduce the mortality rate of breast cancer. Breast thermography is a non-invasive and simple imaging technique used for early detection of breast cancer. Feature extraction and selection of appropriate features play a major role in computer-aided detection of breast cancer using breast thermograms. In this article, texture features are extracted from automatically segmented breast thermograms by computing neighbourhood grey tone difference matrix (NGTDM) and run length matrix (RLM). Significance of these features in differentiating the abnormal breast from the normal breast is found by statistical test. NGTDM extracted coarseness, busyness, complexity, strength and RLM extracted long run emphasis and run percentage are found to be significant by statistical test. Extracted features are computationally less expensive and attained an average accuracy of 80%, sensitivity of 94% and specificity of 71.4% using back propagation neural network classifier.Keywords: asymmetry analysis; breast cancer; breast thermography; neighbourhood grey tone difference matrix; statistical testCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 104 - 118PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089228Issue No:Vol. 14, No. 1/2 (2018)

Authors:Madhavi B. Desai, S.V. PatelPages: 119 - 130Abstract: This paper presents a universal image steganalysis method based on feature selection by principal feature analysis (PFA). The goal of this paper is to increase the performance of existing image steganalysis approaches using PFA-based feature selection method and reduce the high dimensionality of the features used in state-of-the-art steganalysis methods. Principal component analysis (PCA) is widely used in pattern recognition applications. However, PCA has disadvantage that, all the generated features are transformed features. While, PFA selects the subset of preliminary features which contains necessary information. PFA is applied on spatial domain subtractive pixel adjacency matrix features and in case of transform domain, CHEN features (intrablock and interblock Markov-based features) and CC-PEV features (PEV features enhanced by Cartesian calibration). The experimental results show that PFA is effective and efficient in eliminating redundant features. Experimental results prove that the use of PFA method in steganalysis is superior in terms of dimensionality reduction of features and increases the classification performance.Keywords: Feature selection; high-dimensional feature; image steganalysis; image steganography; principal feature analysisCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 119 - 130PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089230Issue No:Vol. 14, No. 1/2 (2018)

Authors:Jobin Varghese, V.M. Akhil, P.K. Rajendrakumar, K.S. SivanandanPages: 131 - 143Abstract: Back-to-sit (BTS) and sit-to-stand (STS) are one among many vital functions of knee joint of human body. While designing bipedal robots, the knee joint should have the capacity to replicate the characteristics, such as that of human beings, especially the torque. The effect of weight and length as well as sitting chair height and speed of movement during BTS and STS has to be studied to limit the weight and height of robots or exoskeleton. Here we have used a camera and motion analysis software to acquire the data and studied the effect and interaction of body mass index and chair height on knee torque at various sitting speeds using two-way analysis of variance.Keywords: BMI; BTS; chair height; STS; torqueCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 131 - 143PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089234Issue No:Vol. 14, No. 1/2 (2018)

Authors:Kalyan Nagaraj, G.S. Sharvani, Amulyashree SridharPages: 144 - 205Abstract: Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.Keywords: big data; bioinformatics; data analyticsCitation: International Journal of Bioinformatics Research and Applications, Vol. 14, No. 1/2 (2018) pp. 144 - 205PubDate: 2018-01-09T23:20:50-05:00DOI: 10.1504/IJBRA.2018.089175Issue No:Vol. 14, No. 1/2 (2018)